Abu Zaher Md. Faridee, Adjunct Assistant Professor
Dr. Faridee’s professional work and research focus on constructing scalable machine learning models resilient against domain and category shifts with minimal-to-no additional supervision. He primarily engages with deep domain adaptation, unsupervised, self-supervised, adversarial, disentangled representation learning, novel category discovery, and learnable data augmentation techniques, with practical applications in text, audio, and video domains. Dr. Faridee aims to discover the optimal transferability of representations across domains, tasks, and modalities, addressing real-world ML challenges with these methodologies.
At Amazon, Dr. Faridee’s team developed an end-to-end neural machine translation pipeline enhancing Amazon’s customer service experience. He enhanced the robustness of NMT models against noisy, out-of-domain inputs using the aforementioned approaches. In 2021, he interned with Microsoft Research’s Audio and Acoustics Research Group, devising novel deep neural architectures for estimating deep noise suppression model performance.
Before obtaining his Ph.D., Dr. Faridee spent approximately 8 years constructing, assembling, and leading teams in distributed data processing and analytics back-ends, catering to millions of users in the USA and UK. From 2009–2013, he actively contributed to several open-source NLP and ML projects through the Google Summer of Code program, both as a participant and later as a mentor.
LinkedIn: www.linkedin.com/in/azmfaridee
Anuradha Ravi, Research Assistant Professor
Dr. Ravi is a Research Assistant Professor in Information Systems at UMBC. Her research interests include edge computing, developing low-power machine learning models for IoT, IoT-enhanced smart spaces, and wireless networking. Prior to joining UMBC, Dr. Ravi worked as a Research Scientist (PostDoc) at Singapore Management University (Oct 2018 – Mar 2023). She completed her Ph.D. at the Indian Institute of Technology, Roorkee, India, in 2016. Dr. Ravi was awarded the “SMU Research Excellence” award for her contribution to the “AI-Based Occupancy Aware Smart Building Energy Optimization project,” which includes indoor localization for occupancy sensing in Smart Buildings. She has vast experience building real-time working prototypes to convert research into real-time demonstration.
Personal Website: https://sites.google.com/view/anuradhar
LinkedIn: https://www.linkedin.com/in/anuradha-ravi-ph-d-36564a3b/
Catherine Ordun, Ph.D. ’23, information systems, Adjunct Assistant Professor
Catherine Ordun is a Vice President at Booz Allen Hamilton leading AI Rapid Prototyping in the Office of the CTO. She graduated in 2023 with a PhD from the Department of Information Systems under Dr. Sanjay Purushotham and Co-advised by Dr. Edward Raff. Her dissertation was entitled “Multimodal Deep Generative Models for Cross Spectral Image Analysis.” Her research interests combine industry innovation and AI development in Multimodal AI, biometrics, and visible-thermal image registration. At Booz Allen, she coordinates with leaders in defense, civil, and health as an AI SME to develop novel and scalable technical approaches. She has also supported the Mark Cuban AI Bootcamp since 2020, which trains high school students to learn the basics of AI. In her spare time, she writes science fiction, runs, and spends time with her family.
LinkedIn: https://www.linkedin.com/in/catherine-ordun
Philip Feldman, M.S. ’14, human-centered computing, and Ph.D. ’20, human-centered computing, Adjunct Assistant Professor
Dr. Feldman has been an Adjunct Research Assistant Professor of Information Systems at UMBC since 2020. He has a diverse and extensive professional background, having served as an AI/ML Futurist at ASRC Federal, Technology Architect at Novetta, Inc, and in various technical and leadership roles at other organizations. His educational background includes a PhD and MS in Human-Centered Computing from UMBC, an MS in Interdisciplinary Studies from Johns Hopkins University, and a BA in Interdisciplinary Studies from the University of Maryland. He has been a speaker at several prominent events and has authored the book “Stampede Theory: Human Nature, Technology, and Runaway Social Realities” published by Elsevier in 2023. Additionally, he has a rich history of publications, speaking engagements, and patents in the fields of AI, machine learning, and human-centered computing. His research interests include societal-scale AI conflict, intelligent control systems, simulation, ethics, and the impact of technology on society.
Personal Website: https://philfeldman.com
LinkedIn: https://www.linkedin.com/in/phil-feldman-phd/
Paul Mulhern, ’96, social work, Visiting Lecturer
Paul Mulhern has over thirty years of experience in healthcare, starting as a nursing assistant in inpatient
psychiatry, then as a licensed social worker in a wide range of healthcare settings. Paul has over
twenty years of health technology and management experience as a program manager as well
as health provider. Paul has seven years of experience as a graduate level educator and course
developer. Paul currently teaches courses in social work policy and clinical practice and teaches
and develops courses in health technology and informatics. Paul continues to provide clinical
services in his own tele-behavioral health practice, managing all billing, claims and technology
aspects of the practice.
Personal Website: https://www.johnpaulmulhernlcswc.com/
Bipendra Basnyat, M.S. ’17, information systems, and Ph.D. ’22, artificial intelligence and machine learning, Adjunct Assistant Professor
Bipendra Basnyat, P.E., PhD is a distinguished AI/ML Solutions Architect with over two decades of experience in designing and implementing advanced artificial intelligence and machine learning systems. Holding a PhD in AI/Machine Learning from the University of Maryland, Baltimore County (UMBC), dual master’s degrees in Information Systems and Civil Engineering, Dr. Basnyat brings a unique blend of academic expertise and industry knowledge to the field.
His career spans roles at prestigious companies like Deloitte, Xerox, and UnitedHealth, where he has consistently driven innovation in AI/ML applications. As an Adjunct Assistant Professor at UMBC, he continues to contribute to academia through teaching, mentoring, and research collaborations. Dr. Basnyat’s technical prowess encompasses various AI/ML frameworks, cloud technologies, and big data platforms, complemented by his entrepreneurial spirit, having founded three startups. His work has been recognized through successful NSF-funded projects and publications in reputable international conferences, particularly in Computer Vision. Currently, Dr. Basnyat is leveraging his expertise to address Human-Wildlife Conflict using AI, exemplifying his commitment to applying cutting-edge technology to real-world challenges.
LinkedIn: https://www.linkedin.com/in/bipendra-basnyat-p-e-phd-65795a2a/
Zahid Hasan, Ph.D ’24, artificial intelligence and machine learning, Postdoctoral Research Fellow
Zahid Hasan is a postdoctoral research associate at the University of Maryland, Baltimore County (UMBC). He earned his Ph.D. in the MPSC lab, Information Systems department, under Professor Nirmalya Roy. Zahid’s research interests include learning under data uncertainty, self-supervised learning, semi-supervised learning, developing robust deep-learning frameworks, and leveraging data priors to learn from unlabeled data. He works on designing deep learning models for remote photoplethysmography (rPPG) to extract vital signs from video signals. Zahid also investigates video action recognition, focusing on view-invariant feature learning and novel class discovery, aiming to tackle uncertainties in video datasets and advance contactless health monitoring and action recognition.
Personal Website: https://mxahan.github.io/digital-cv/
Elizângela de Souza Rebouças, Postdoctoral Fellow
Dr. Elizângela de Souza Rebouças is a researcher and educator specializing in image processing, AI, and machine learning. She is currently a Professor at Federal Institute of Ceará (IFCE) and a researcher at LAPISCO (lapisco.ifce.edu.br). Dr. Rebouças holds a PhD in Teleinformatics Engineering from Federal University of Ceará (2022), with research focused on medical image segmentation using advanced computational techniques. She also earned a Master’s in Telecommunications Engineering from IFCE (2017), and holds specializations in Education, Project Management, and Software Engineering. Dr. Rebouças is a Research Scholar at UMBC, working in the SONG LAB under the supervision of Dr. Houbing Hebert Song.
Her projects and research have led to numerous publications, and she has successfully contributed to AI-driven solutions across various domains, including medical diagnostics and public security.
LinkedIn: linkedin.com/in/elizangela-souza-reboucas
Lattes CV: http://lattes.cnpq.br/1153256262452288
Pedro Pedrosa Rebouças Filho, Postdoctoral Research Fellow
